1. Field of the Invention
This invention generally relates to selecting one or more parameters for a defect detection method applied to semiconductor wafer inspection.
2. Description of the Related Art
The following description and examples are not admitted to be prior art by virtue of their inclusion in this section.
Inspection processes are used at various steps during a semiconductor manufacturing process to detect defects on wafers. Semiconductor foundries manufacture various integrated circuit (IC) products for numerous fabless semiconductor companies. Defect types are different on different devices and layers. Inspection for many different types of defects has become more important recently. In some instances, a system that is configured to detect different types of defects may have adjustable sensitivity (or defect detection) parameters such that different parameters can be used to detect different defects or avoid sources of unwanted (nuisance) events. The efficiency in finding the right set of sensitivity parameters for a specific inspection objective is critical to inspection recipe setup.
Although an inspection system that has adjustable sensitivity parameters presents significant advantages to a semiconductor device manufacturer, these inspection systems are essentially useless if incorrect sensitivity parameters are used for an inspection process. Inspection sensitivity optimization is performed manually today. The user classifies defects, which serve as the “ground truth,” specifies sensitivity parameters based on classified defects, runs inspection or inspection simulation such as Visual Optimizer (VO), which is commercially available from KLA-Tencor, Milpitas, Calif., and then looks at the inspection result. If the result is not satisfied, another round of parameter tuning is performed. VO provides visual feedback of the defect count or density distribution as well as the inspection simulation results. This parameter tuning process is performed iteratively until the result satisfies the user's objective, which is usually measured by defect of interest (DOI) capture and nuisance suppression. If the result cannot satisfy the detection objective, the user may change inspection imaging mode, such as spectrum, aperture, pixel size, focus, scan speed, etc. Another round of sensitivity tuning will be performed. This larger loop is also an iterative process. The number of iterations in the parameter tuning loop (inner loop) is orders of magnitude larger than combinations of the two outer loops. Automatic segmented auto thresholding (AutoSAT), which is commercially available from KLA-Tencor, can optimize SAT inspection recipes automatically.
As the semiconductor design rule is shrinking, DOI become smaller and smaller. To find such defects, more sophisticated defect inspection algorithms and more specialized optical modes are required. It is more and more difficult for general inspection tool users to fully comprehend and effectively tune the algorithms, especially in cases where a single optical mode does not adequately detect the DOI. Because of this complexity, the recipe set up time is getting longer and quality is much more subject to human-related factors, such as knowledge, experience, and skills. Inexperienced users may easily produce inconsistent or low quality recipes.
Multi-channel and multi-pass inspections challenge even experienced users. For example, there are three detection channels in Puma, a dark field inspection tool, commercially available from KLA-Tencor. The user currently sets up the recipe parameters channel by channel. If a defect is detected in one channel, the defect is detected by the inspection. It is not necessary to detect the same defect in another channel. It is almost impossible for a human user, taking the channel-by-channel approach, to fully leverage the parameter value in two other channels when tuning a parameter in the current channel. This approach limits the user's ability to take advantage of multi-channel inspection. Each iteration of manual tuning of a sensitivity recipe usually takes from many minutes to hours. To set up a production recipe, the user may go through many iterations, which may take days to complete.
AutoSAT is the first attempt to address the issue of automatic tuning sensitivity recipes. It optimizes the threshold parameters on a segment-by-segment basis. Here, a “segment” refers to a group of pixels within an image gray level range. The segment parameters are determined manually before AutoSAT is run. Its optimization engine cannot optimize segments and thresholds simultaneously. Since it does exhaustive searching in many sub-spaces of the full parameter set, it does not address the issue of recipe stability. AutoSAT is currently limited to the SAT algorithm. It cannot be applied to other algorithms.
Accordingly, it would be advantageous to develop methods and/or systems for selecting one or more parameters for one or more defect detection methods or algorithms that do not have one or more of the disadvantages described above.